Nonparametric kernel estimation of evolutionary autoregressive processes
AbstractThis paper develops a new econometric tool for evolutionary autoregressive models where the AR coefficients change smoothly over time. To estimate the unknown functional form of time-varying coefficients, we propose a mdified local linear smoother. The asymptotic normality and variance of the new estimator are derived by extending Phillips and Solo device to the case of evolutionary linear processes. As an application for statistical inference, we show how Wald tests for stationarity and misspecification could be formulated based on finite-dimensional distributions of the kernel estimates. We also examine the finite sample performance of the method via numerical simulations. As an empirical illustration, the method is applied to the real data of US stock returns. --
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Bibliographic InfoPaper provided by Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes in its series SFB 373 Discussion Papers with number 2001,103.
Date of creation: 2001
Date of revision:
Autoregressive models; Evolutionary linear processes; Local linear fits; Locally-stationary processes; Phillips and Solo device; Time-varying coefficients;
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- Cai, Zongwu, 2007. "Trending time-varying coefficient time series models with serially correlated errors," Journal of Econometrics, Elsevier, vol. 136(1), pages 163-188, January.
- Dahlhaus, Rainer, 2009. "Local inference for locally stationary time series based on the empirical spectral measure," Journal of Econometrics, Elsevier, vol. 151(2), pages 101-112, August.
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